Journal of Geophysical Research: Atmospheres

The cause of the seasonal variation in the oxygen isotopic composition of precipitation along the western U.S. coast


  • Nikolaus H. Buenning,

    Corresponding author
    1. Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
      Corresponding author: N. H. Buenning, Department of Earth Sciences, University of Southern California, Zumberge Hall of Science, 3651 Trousdale Pkwy., Los Angeles, CA 90089-0740, USA. (
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  • Lowell Stott,

    1. Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
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  • Kei Yoshimura,

    1. Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan
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  • Max Berkelhammer

    1. Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
    2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
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Corresponding author: N. H. Buenning, Department of Earth Sciences, University of Southern California, Zumberge Hall of Science, 3651 Trousdale Pkwy., Los Angeles, CA 90089-0740, USA. (


[1] This study seeks to find the primary influence on the seasonal cycle of the oxygen isotopic composition of precipitation (δ18Op) along the western U.S. coast. Observed long-term mean seasonal variations ofδ18Op from 16 different stations along the west coast are presented. The most robust features in the observations are high values in the summer and a drop in δ18Opduring the winter. The Isotope-incorporated Global Spectral Model (IsoGSM) also simulates this wintertime drop inδ18Op along the west coast of the U.S. Sensitivity experiments are performed with IsoGSM where individual oxygen isotope fractionation processes are turned off. These simulations reveal that the primary control on the seasonal variations is equilibrium oxygen isotopic fractionation during vapor condensation. There is almost no influence of the temperature dependence of equilibrium fractionation on the seasonal δ18Op cycle for both evaporation and condensation. Additional experiments (including tagging simulations) are performed to better understand why Rayleigh distillation causes the seasonal variation in δ18Op. The tagging simulations and budget calculations reveal that vertical oxygen isotope gradients and variations in condensation height cause the seasonal cycle in δ18Op. This results from seasonal changes in the polar jet, and subsequent changes to divergence and vertical velocities, which affects the uplift of moisture. These findings suggest that δ18Op in the western U.S. is a tracer of condensation height on seasonal timescales. The large influence of condensation height on δ18Op seasonality complicates interpretations of interannual climate proxy records based on isotopes in precipitation as the seasonality is likely not static.

1. Introduction

[2] The 18O/16O and D/H compositions of precipitation are commonly used in studies of hydrology [Lee et al., 1999; Kendall and Coplen, 2001; Vachon et al., 2007] and past climate variability [Dansgaard, 1964; Dansgaard et al., 1969; Lorius et al., 1979; Grootes et al., 1993; Thompson et al., 1995]. Such studies make use of observed correlations associated with precipitation that carries lower 18O/16O and D/H ratios with increasing precipitation amount (the amount effect) and higher ratios with increasing temperature (the temperature effect). Studies have shown that the amount effect is related to diffusive exchanges of re-evaporated precipitation with background vapor and recycling of vapor within convective fluxes [Lee et al., 2007; Worden et al., 2007; Risi et al., 2008]. The temperature effect comes about from the influence of temperature on the saturation vapor pressure and preferential rainout of the heavier isotopologue [Dansgaard, 1964; Hendricks et al., 2000; Kavanaugh and Cuffey, 2003]. However, it is possible that for some regions the correlation between temperature and the isotopic composition of precipitation may be an artifact of other processes that are either indirectly related to or in phase with temperature variations [Dansgaard, 1964; Noone, 2008].

[3] Several studies have suggested that the statistical correlation between the isotopic composition of precipitation (hereafter denoted as δ, where δ = R/RVSMOW − 1, R is the heavy to light isotope ratio, and RVSMOW is the Vienna Standard Mean Ocean Water) and temperature and precipitation amount could arise from variations in the seasonal timing and spatial characteristics of moisture advection [Fricke and O'Neil, 1999; Werner et al., 2000; Alley and Cuffey, 2001; Brown and Simmonds, 2004; Noone, 2008; Feng et al., 2009]. These include studies focused on the western coast of the U.S., where the variations in precipitation δ values have been linked to changes in moisture source on weekly to seasonal timescales [Friedman et al., 1992, 2002a, 2002b; Berkelhammer et al., 2012] and to condensation height [Coplen et al., 2008] and rainfall re-evaporation [Yoshimura et al., 2010] on shorter (sub-hourly) timescales. Examining 6 month integrated measurements of precipitationδ values; Friedman et al. [1992] concluded that storm trajectories were the primary driver of seasonal variations in δ values in the western U.S. On the other hand, in southwestern Oregon, Ersek et al. [2010] found no connection between δ values and storm trajectories, but high correlations with temperature. Similarly, Vachon et al. [2010a] noted the possibility that variations in source water temperatures and/or the temperatures of condensation might explain the observed seasonality in precipitation δ values within the U.S. mainland. However, they also noted that these factors require use of atmospheric General Circulation Models to confirm this hypothesis.

[4] The present study seeks to understand whether or not the temperature of condensation, temperature of evaporation (i.e., the temperature of the marine source region), or other processes (such as storm trajectory, condensation height, or kinetic effects) control the seasonal cycle in precipitation δ values along the western U.S. coast. This study also aims to quantify the contribution that the various fractionation processes have on the regional isotopic seasonal cycle.

[5] In the following section both the observational data and the isotope-incorporated General Circulation Model (GCM) used to investigate the isotope seasonality are described (section 2). The observations are then presented and used to validate the GCM (section 3). Sensitivity experiments are conducted with the model to understand which fractionation process has the largest influence on the seasonal isotope variations within the model (section 4). Additional simulations and calculations are then presented to better understand the exact cause of the modeled seasonal cycle (section 5). Finally, the paper concludes with a summary of the results and a discussion of the implications of the findings (section 6).

2. Methods

2.1. Observations

[6] To better quantify the observed seasonal cycle in precipitation δ values, archived precipitation samples were obtained from 9 stations along the western U.S. coast (7 in California, one in Oregon, and one in Washington, Figure 1). The samples were collected from 2001 through 2006 and come from the National Atmospheric Deposition Program (NADP). Previous work has demonstrated that the NADP collection and archiving protocol is sufficient for isotopic analysis [Harvey and Welker, 2000; Welker, 2000; Harvey, 2001; Vachon et al., 2007, 2010b, 2010a; Berkelhammer et al., 2012]. A total of 848 precipitation samples were used for this study, which include the measurements made by Berkelhammer et al. [2012]. The previous water samples were analyzed by continuous flow IRMS using a Thermofinnigan TC/EA and Delta Plus XP mass spectrometer. The water samples not included by Berkelhammer et al. [2012]were measured at the Stott Paleoclimate Laboratory at the University of Southern California using a Picarro Cavity Ring-down Spectrometer. Monthly mean isotopic seasonal cycles in precipitationδ values are presented here, along with the values reported by Vachon et al. [2010a] (hereafter V10a) and data from two stations within the Global Network of Isotopes in Precipitation (GNIP). GNIP is a network run by the International Atomic Energy Agency and the World Meteorological Organization ( Thus, this study has assembled the most complete set of precipitation isotope measurements along the western U.S. coast to date. A complete list of the stations used in this study is given in Table 1.

Figure 1.

Map showing station locations for the GNIP sites (green), the west coast NADP sites used by Vachon et al. [2010a] (blue), and the NADP sites used here and by Berkelhammer et al. [2012] (red). Sites used by both NADP studies are represented as a blue circle surrounded by a red square.

Table 1. Name and Information of Collection Sites
Station NameLongitude (°W)Latitude (°N)Elevation (m)Source
Victoria123.4348.6520GNIP, (
WA14, Olympic NP123.932547.8597182This study and Vachon et al. [2010a]
WA24, Palouse Conserv. Farm117.184746.7606766Vachon et al. [2010a]
OR18, Starkey Exp. Forest118.51345.22471254Vachon et al. [2010a]
OR97, Hyslop Farm123.1944.634769This study
OR02, Alsea Guard Ranger123.385644.38356104Vachon et al. [2010a]
OR10, H. J. Andrews Exp. For.122.25644.2118443Vachon et al. [2010a]
CA50, Sagehen Creek120.239739.43151931This study
CA45, Hopland123.08639.0045253This study and Vachon et al. [2010a]
CA99, Yosemite119.858137.79611393Vachon et al. [2010a]
CA95, Death Valley NP116.97836.589125This study
CA75, Sequoia NP118.77836.56611921This study
CA66, Pinnacles NM121.15736.4834317This study
Santa Maria120.4534.979GNIP, (
CA42, Tanbark Flat117.761834.2071853This study
CA67, Joshua Tree NP116.388934.06951239This study

[7] For brevity, this study focuses on the 18O/16O composition of precipitation (δ18Op is used for both observed and modeled values hereafter). V10a report mean monthly δ18Op for samples collected from 1989 to 1995. The additional NADP data reported here come from weekly samples. The measurements are converted to monthly means by weighting each sample within a month according to precipitation amount. The δ18Op values are applied to both months when a week begins in one month and ends in another, though the precipitation amount is divided based on how many of the days fall in each month for that given week. In other words, the precipitation is assumed to have fallen equally during each day of the week.

2.2. Model

[8] The principal tool used to interpret the seasonal isotopic variability of precipitation is the Experimental Climate Prediction Center's Global Spectral Model fit with isotope tracers (IsoGSM) [Yoshimura et al., 2008]. The atmosphere in IsoGSM is represented by 28 vertical σlayers (unit-less) withσ = 0.002 representing the uppermost layer. The horizontal resolution of the model is given by triangular truncation of the spherical harmonic spectrum at wave number 62, which corresponds to a Gaussian grid of about 1.85 degree longitude x 1.85 degree latitude. The simulations use a relaxed Arakawa-Schubert scheme [Moorthi and Suarez, 1992] for parameterizations of moist convection. The model is forced with prescribed sea surface temperatures and sea-ice conditions from the optimal interpolation weekly data set, downloaded from the Experimental Climate Prediction Center database [Reynolds and Smith, 1994]. Simulations were spectrally nudged at six-hour intervals to wind and temperature fields from the National Center for Environmental Prediction and National Center for Atmospheric Research Reanalysis version 1 [Kalnay et al., 1996]. The spectral nudging technique converts spectrum base meteorological data to Fourier series, and nudges the Fourier coefficients to that of the reanalysis (the specific details are described in Yoshimura and Kanamitsu [2008]). This approach constrains the dynamic wind and temperature fields in the model to closely match reanalysis fields. Furthermore, the technique results in simulated precipitation that is sufficiently accurate to allow direct comparison between model simulations and historical isotopic observations on event timescales [Berkelhammer et al., 2012]. The present study investigates the model's ability to capture the long-term mean seasonal variations inδ18Op along the western U.S. coast.

[9] Water isotopoogues are accounted for in IsoGSM in all three phases within the simulated atmosphere, and fractionation factors (α = Rcd/Rg, where subscripts cd and g refer to condensed phase and gas) are calculated and applied when phase changes occur. IsoGSM estimates equilibrium oxygen isotopic fractionation during condensation in the atmosphere (αeq-con) and both ocean (αeq-ev) and raindrop evaporation (αeq-rev) according to the equations given by Majoube et al. [1971a, 1971b]. Ocean water in IsoGSM is assumed to have a constant δ value of 0 ‰. Evaporation and isotopic exchange from falling raindrops in IsoGSM is estimated following Stewart [1975]. It is assumed that 95% of falling rain isotopically equilibrates with surrounding vapor for stratiform precipitation and 45% for convective precipitation. The formulation of Stewart [1975] for kinetic fractionation during raindrop evaporation is implemented into IsoGSM. The model also accounts for kinetic fractionation during ocean evaporation (αk-ev) [Merlivat and Jouzel, 1979] and during vapor deposition onto ice crystals (αk-con) under supersaturated conditions [Jouzel and Merlivat, 1984].

[10] An unperturbed control simulation (CTRL) is first compared to observations. The model is then used to quantify contributions to the simulated seasonal δ18Op cycle from certain isotope fractionation processes, similar to the experiments shown by Noone and Sturm [2010] (a complete list of each simulation's name and description is given in Table 2). To this end, a total of 8 simulations were performed (name in all capitals and in parenthesis), where fractionation factors (the α variables defined above) are set to one. Two simulations remove the influence of equilibrium oxygen isotopic fractionation processes [Majoube, 1971a, 1971b] in order to quantify how each isotope effect contributes to the simulated seasonal δ18Op cycle. These processes are equilibrium oxygen isotopic fractionations associated with ocean water evaporation (αeq-ev, NOFEQ1) and condensation in the atmosphere (αeq-con, NOFEQ2). Because setting αeq-ev and αeq-con equal to 1 removes both the temperature dependence and the isotope effect (i.e., preferential rainout or evaporation), additional simulations are conducted in which the equilibration temperature is assigned a constant value, globally (CONFEQ1 and CONFEQ2 with αeq-ev = 1.00980653 and αeq-con = 1.01162795, respectively). Another simulation is configured so that raindrops do not evaporate in isotopic equilibrium [Stewart, 1975]. In this case, both equilibrium and kinetic oxygen isotopic fractionations (αeq-rev and αk-rev, respectively) and isotopic exchanges that occur during evaporation from droplets are turned off in the model (NORNEV) [Bony et al., 2008], similar to the experiments of Wright et al. [2009], Yoshimura et al. [2010], and Field et al. [2010a]. Simulations that remove kinetic isotopic fractionation are also conducted, which included isotopic fractionation during ocean evaporation (αk-ev NOFKI1) [Merlivat and Jouzel, 1979] and vapor deposition onto ice crystals (αk-con, NOFKI2) [Jouzel and Merlivat, 1984]. Discussed in more detail in section 5.2, two tracer simulations are also performed (TAGLAT, TAGLEV), where moisture is tagged within predefined regions (see Figure 5a) for the purpose of understanding how changes in moisture source and changes in condensation height influence the seasonal variations in δ18Op. The tagging is done such that the amount of tagged vapor is always equal to the simulated vapor amount within a predefined boxed region. Each IsoGSM simulation runs from 1953 through 2010 with a 10-min time step, and each simulation is nudged to the same reanalysis wind fields [Kalnay et al., 1996].

Table 2. Name and Description of IsoGSM Simulations
Simulation NameDescription
CTRLUnperturbed control simulation
NOFEQ1Equilibrium oxygen isotopic fractionation during ocean water evaporation is turned off (αeq-ev = 1)
NOFEQ2Equilibrium oxygen isotopic fractionation during condensation is turned off (αeq-con = 1)
NORNEVAll oxygen isotopic fractionation associated with raindrop evaporation is turned off
CONFEQ1Equilibrium oxygen isotopic fractionation during ocean water evaporation is set to constant, removing the temperature dependence (αeq-ev = 1.00980653, T = 293 K)
CONFEQ2Equilibrium oxygen isotopic fractionation during condensation is set to a constant, removing the temperature dependence (αeq-con = 1.01162795, T = 274 K)
NOFKI1Kinetic oxygen isotopic fractionation during ocean water evaporation is turned off (αk-ev = 1)
NOFKI2Kinetic oxygen isotopic fractionation during vapor deposition onto ice is turned off (αk-con = 1)
NOLOC37Equilibrium oxygen isotopic fractionation associated with condensation is turned off (αeq-con = 1) at one grid-cell: 37.1 N and 124 W.
NOLOC41Equilibrium oxygen isotopic fractionation associated with condensation is turned off (αeq-con = 1) at one grid-cell: 41.1 N and 126 W.
NOLOC45Equilibrium oxygen isotopic fractionation associated with condensation is turned off (αeq-con = 1) at one grid-cell: 45.1 N and 126 W.
NOLOC49Equilibrium oxygen isotopic fractionation associated with condensation is turned off (αeq-con = 1) at one grid-cell: 49.1 N and 126 W.
TAGLATTagging simulation where tag1 is applied within 10 N–30 N and 180–210 W. Tag 2 is applied within 40 N–60 N and 200 W–230 W.
TAGLEVTagging simulation where both tags are applied within 25 N–55 N and 200 W–245 W. Tag 1 is applied below the 0.85 σ level and tag 2 is above the 0.85 σ level.

3. Observed and Simulated Seasonal Cycle

[11] Long-term mean seasonal cycles in precipitationδ18O values are presented here to characterize the amplitude and phase of monthly δ18Op variability along the western U.S. coast (all monthly means presented here can be downloaded at:∼buenning/d18O-season/index.html). Figures 2a and 2p show the observed average cycles from the two GNIP stations, which are located on a middle latitude (Victoria, 48.65°N) and a subtropical latitude (Santa Maria, 34.9°N). At both stations the highest δ18Op values occur during warm summer months and the lowest values during cool winter months, a middle latitude observation that is consistent with many other studies [Gonfiantini and Picciotto, 1959; Dansgaard, 1964; Rozanski et al., 1982; Feng et al., 2009; Ersek et al., 2010]. These types of variations would be expected if evaporation temperatures were the primary control on seasonal δ18Op variations (i.e., cooler Pacific sea surface temperatures occurring in the winter). However, the phases and the amplitudes of the seasonal cycle differ between the two sites. For example, at Santa Maria the highest δ18Op occurs in July and then δ18Op drops sharply to a minimum value in September, whereas at Victoria δ18Op increases more or less progressively from early spring through the summer months, reaching a maximum in October and then decreases to a minimum in February (Figure 2a). The seasonal amplitude in δ18Op is also larger at Santa Maria, where the mean seasonal range is 7.3 ‰, compared to 4.0 ‰ at Victoria. It is important to note that this discrepancy in seasonal amplitudes is inconsistent with a regional temperature effect, as seasonal temperature ranges are much higher at Victoria than at Santa Maria (not shown).

Figure 2.

Long-term mean observed seasonal cycles ofδ18Op (black dashed line) and simulated seasonal cycles from ISOGSM (blue). Error bars indicate one standard deviation.

[12] Vachon et al. [2010a] reported mean seasonal δ18O cycles for 7 stations located in Washington, Oregon, and California. Here we combine the data from V10a with data for 9 additional NADP stations (2 of which were also reported by V10a), including the data from Berkelhammer et al. [2012] (Figure 2). Two of the NADP stations (CA45 and WA14) are shown twice to account for the different years that V10a and this study measured rain samples (Figures 2b, 2c, 2j, and 2k). Consistent with the Victoria data, each of the northern NADP stations exhibits their highest δ18Op values in late summer and lowest δ18Opduring the winter months. Many of the northern stations also have a semi-annual cycle, with two peaks. The stations in California have highestδ18Op values earlier than those in Oregon and Washington, typically in July. One feature of the seasonal cycle that is consistent among each of the western U.S. stations is a seasonal drop in δ18Op during winter months (Figure 2). The Hopland, California station (CA45) from V10a is the exception to this pattern (Figure 2j), with the lowest δ18Op values in September, followed by a sharp increase in values in October. Winter values at this site are close to the annual mean from 1989 to 1995.

[13] The mean seasonal cycle for the CTRL IsoGSM simulation is also calculated (Figure 2) using only the years when samples were collected. At many of the stations the mean simulated δ18Op values are less negative compared to the observations. Berkelhammer et al. [2012] found that the model's coarse topographic resolution was responsible for the overall high values of δ18Op simulated by IsoGSM, which has been found in other models [Schmidt et al., 2005] and can be seen at most high elevation locations (Table 1) in Figure 2. The course resolution causes the elevation in the model to be lower than the station elevation. The lower elevation causes higher δ18Op because the falling raindrops are subject to more enrichment in 18O via evaporation and equilibration with surrounding vapor that have enriched δ values relative to vapor at higher elevations. The relatively coarse resolution of IsoGSM can also cause problems when geographical structures have large influences on regional δ18Op, such as coastline resolution for coastal grid cells and vegetation classification for inland grid cells.

[14] To quantify the model's ability to capture the mean seasonal δ18Op cycle, correlation coefficients between the modeled and observed values are calculated for each panel in Figure 2. Error bars in Figure 2represent the standard deviation of the individual monthly means that went into calculating the long-term averages for both the model and observations. Despite the model limitation listed above, the model simulates the seasonal variations at most west coast stations very well, while failing to simulate the variations at others. For example, at Hyslop Farm, Oregon (OR97) and Sagehen Creek, California (CA50) the correlation coefficient between mean seasonalδ18Opobservations and the model is about 0.9. The model also accurately simulates the sub-seasonalδ18Op variations at the Hopland station (CA45), especially the low δ18Op in September that was reported by V10a (Figure 2j). IsoGSM does not capture the seasonal cycles reported by V10a at Olympic, Washington (WA14) and Yosemite, California (CA99). The model also performs poorly at Death Valley. The data/model discrepancies at Yosemite and Death Valley are likely a result of complex topography in the region that is not resolved in the model. The discrepancy at Olympic appears to be due to one month (September) when the model simulates anomalously low δ18Op.

[15] Figure 3a shows the 1953 to 2010 simulated mean seasonal cycles for five grid cells along the coast of western North America. At the southernmost grid cell there are large δ18Op minima in July and September. The minimum in July should be viewed with caution as there is no southern California station data with such a drop in δ18Op during July and the low value occurs during the region's dry season. The low simulated δ18Op value in September is consistent with low values observed at Santa Maria, California. Some of the general features of the observational record are also found in the model simulation. For example, the seasonal amplitude in δ18Opis larger and the maximum occurs earlier at the southern-most stations and grid cells. Also, the model simulates a seasonal drop inδ18Op during winter months along the west coast, a feature that is also observed at all but one of the observational stations. This study now turns to understanding the cause of the simulated seasonal drop in δ18Op.

Figure 3.

Long-term mean (1953–2010) simulated seasonal cycles of (a)δ18Op and (b) δ18OPW for grid cells located along the western North American coast (Figure 3a). The southernmost grid cell (red) is located in southern California, and the northern most grid cell (violet) is located in British Columbia.

4. Sensitivity of Isotope Seasonality

[16] In this section a set of sensitivity experiments (Table 2) are presented to evaluate the processes that cause the simulated wintertime drop in δ18Op along the west coast of North America. The sensitivity experiments test whether the seasonal change in δ18Op is due to temperature of evaporation and/or temperature of condensation (i.e., the temperature dependence of equilibrium fractionation), a hypothesis put forth by V10a. These simulations are also used to assess whether the seasonality is due to isotopic fractionation during Rayleigh distillation (rainout), raindrop evaporation, or kinetic processes. Each model experiment is compared to the control simulation (CTRL) to assess which experiment results in a reduced seasonal cycle (i.e., Does the removal of a fractionation process remove the simulated seasonal δ18Op cycle?).

[17] For brevity, results of the sensitivity experiments are shown for only one location along the western U.S. coast, though the same general results were consistently found north and south of this one location (complete model results can be downloaded at∼buenning/d18O-season/index.html). Figure 4 illustrates the model results for the grid cell centered at 42.856°N and 123.75°W, near Glendale, Oregon. This location is selected because it is centrally located on the west coast. Removing the equilibrium oxygen isotopic fractionation associated with ocean water evaporation (NOFEQ1) only slightly modifies the simulated wintertime drop in δ18Op (Figure 4a). Also, removing the temperature dependence of the equilibrium isotopic fractionation factor (CONFEQ1) results in almost no change to the δ18Op seasonality. These results indicate that both the equilibrium isotopic fractionation during ocean water evaporation and the temperature dependence of the fractionation factor did not substantially contribute to the seasonal wintertime drop in δ18Op within IsoGSM.

Figure 4.

Simulated long-term mean (1953–2010) seasonal cycles ofδ18Opanomalies (relative to the annual mean) for a grid-cell located at coastal Oregon, centered near Glendale, Oregon (123.75°W, 42.856°N). The control simulation is represented by the solid blue curves. (a and b) Comparison of the control simulation with sensitivity experiments that turn off and set constant the equilibrium fractionation during ocean evaporation (Figure 4a) and vapor condensation (Figure 4b). (c and d) Comparison of the control simulation with sensitivity experiments that removes isotope effects associated with rain evaporation and post condensation exchange (Figure 4c) and kinetic fractionation processes (Figure 4d). Each seasonal cycle has the annual mean removed for better comparison.

[18] The largest model response is the removal of equilibrium isotopic fractionation during vapor condensation (NOFEQ2, Figure 4b). In particular, the seasonal drop in δ18Op during winter months does not occur for the NOFEQ2 simulation. These results illustrate how the modeled seasonal cycle in the western U.S. is almost completely caused by equilibrium isotopic fractionation associated with condensation of vapor (i.e., Rayleigh distillation during rainout), and Figure 4 illustrates how the other fractionation processes have only a small contribution to the simulated seasonal δ18Opcycle. Like evaporation, this isotopic fractionation factor is temperature-dependent. Results from the CONFEQ2 (which sets the fractionation factor to a constant value) results in a slight increase in the seasonal amplitude. This is not a surprising result since warmer atmospheric temperatures will result in a decrease in theδ18O value of condensed phase water (under equilibrium conditions) [Majoube, 1971a]. Thus, seasonal variations in temperature and the temperature dependence of equilibrium fractionation during condensation act to decrease the simulated seasonal amplitude of δ18Opin the western U.S. (and likely most non-tropical regions). The exact reason why Rayleigh distillation causes the large seasonal variations inδ18Op will be explored further in section 5.

[19] Removing isotope effects associated with raindrop evaporation (NORNEV) only slightly changes the modeled seasonal amplitude in δ18Op along the western U.S. coast (Figure 4c). The largest difference between the NORNEV and CTRL simulations occurs during fall and spring months (this cannot be seen from Figure 4c because the annual mean is removed) when δ18Op is lower for the NORNEV simulation. This result indicates there is significant evaporative enrichment of raindroplets prior to and after the wet season. These results contrast with other studies [Wright et al., 2009; Field et al., 2010a; Noone and Sturm, 2010; Yoshimura et al., 2010] who performed similar experiments to the NORNEV simulation and found that post condensation processes had a large influence on the spatial and temporal variation of δ values, though these studies were not focused on isotope seasonality in the western U.S.

[20] Removing the kinetic isotopic fractionation associated with ocean evaporation (NOFKI1) slightly reduces the simulated seasonal cycle of δ18Op (Figure 4d). The seasonal cycle of δ18Op is also slightly reduced when kinetic isotope fractionation during vapor deposition onto ice crystals is turned off (NOFKI2, Figure 4d). These results suggest that both kinetic effects contribute slightly to the observed seasonal δ18Op variations. As such, the residual seasonal cycle in the NOFEQ2 experiment (Figure 4b) is partially a result of these kinetic effects.

5. Reconciling the Sensitivity Experiments

5.1. Additional Simulations

[21] The main result of the sensitivity experiments (the NOFEQ2 experiment) was the near complete dampening of the simulated seasonal δ18Op cycle along the western U.S. coast when removing equilibrium isotopic fractionation during condensation. Four mechanisms could be responsible for this result. First, the seasonal variations could be a result of local rainout. Second, the NOFEQ2 simulation changes the horizontal spatial patterns of the isotopic composition of water vapor (δ18Ov), such that the equator-to-pole gradient is greatly reduced (Figure 5). If the winter drop in δ18Op was a result of more moisture advection from the middle latitudes during winter, a reduced latitudinal isotopic gradient would reduce the seasonal δ18Op amplitude. Third, the NOFEQ2 simulation also changes the vertical gradient of δ18Ov. In the CTRL simulation, δ18Ovsharply decreases away from the surface; however, this surface-to-tropopause gradient becomes almost flat when Rayleigh distillation is removed in the NOFEQ2 simulation (Figure 6). Thus, a seasonal change in the condensation height could cause the simulated drop in δ18Op. Fourth, the seasonal variations could be the result of the temperature dependence of αeq-con and the seasonal change to the temperature of condensation. However, this hypothesis was disproved by results of the CONFEQ2 experiment.

Figure 5.

Annual mean δ18OPW for (a) the control (CTRL) simulation and (b) the simulation that turns off equilibrium fractionation during vapor condensation. Color contour intervals are 1‰, and black contour intervals and 2‰. Boxed regions in Figure 5a indicate the tagging regions for the TAGLAT simulation.

Figure 6.

Simulated annual mean vertical profile of δ18Ov for the control simulation (CTRL, blue) and the simulation that turns off equilibrium fractionation during vapor condensation (NOFEQ2, yellow). The profiles are taken from a grid cell near Santa Cruz, California.

[22] To investigate which of these mechanisms have the greatest influence on the simulated seasonal δ18Op cycle, additional model simulations are performed (Table 2). To assess the role of local rainout, 4 simulations are conducted where condensation equilibrium isotopic fractionation is turned off at a single grid cell for each simulation (NOLOC37, NOLOC41, NOLOC45, NOLOC49, corresponding to grid cells at latitudes of 37°N, 41°N, 45°N, and 49°N, respectively).

[23] Two tracer simulations are performed where water vapor is “tagged” within predefined boxed regions, in order to assess how horizontal and vertical δ18Ov gradients influence the seasonal δ18Op cycle. For each of these simulations the “tagged” mixing ratios are set to the “normal vapor” mixing ratio at each grid cell at each time step within the predefined boxed region. Once the vapor tags are added to the atmosphere they are treated like normal water molecules (i.e., they are allowed to advect, mix, condense, and rainout in the atmosphere), though they are not factored into energy budget calculations. Thus, the model simulates and outputs tagged vapor concentrations and tagged precipitation. For the first tagging simulation (TAGLAT), two water vapor tags are added within two regions: one within the tropics/subtropics near Hawaii and another in the Gulf of Alaska (Table 2 and Figure 5a). The second tracer simulation (TAGLEV) tags vapor within the western U.S. (tagging region: 150°W–115°W, 25°N–67°N) according to the vertical level. One tag is applied below the 0.85 σ level (typically higher vapor δ values) and one above (lower δ values). Above the ocean (mean sea level pressure), the vertical tags are located at the surface to 854 mb for the lower level tags and 854 mb to 2 mb for the upper level tag. The 0.85 σlevel was chosen after a trial-and-error process for maximizing the seasonal variation in “tagged” precipitation fraction along the western U.S. coast. For the TAGLEV simulation, “tagged” vapor that advects across the 0.85σ level or outside of the western U.S. coast region is immediately removed. This ensures that the precipitated tags reflect the condensation height. However, slight errors may result from horizontal flow of condensed phase water into the tagging region, which could cause the sum of tagged precipitation at a given point to be less than normal precipitation.

[24] Removing the condensation equilibrium isotopic fractionation at single grid cells (process 1) causes the simulated mean δ18Op to decrease by about 10 ‰. This demonstrates that (in the model) most of the condensation and precipitation occurs locally. However, each of the NOLOC simulations results in a slight increase in the δ18Op seasonal amplitude (Figure 7). Even when the region of no fractionation was symmetrically expanded (by as much as 10 degrees of longitude and latitude), the simulated seasonal cycle remains nearly unchanged (not shown). These findings imply that the simulated seasonal δ18Op cycles are not caused by local rainout or rainout to the west of the U.S. coast, but are likely a consequence of either the horizontal or vertical gradients in δ18Ov (Figures 5 and 6) that result from Rayleigh distillation occurring at all locations.

Figure 7.

Changes to the long-term mean seasonal cycle when local equilibrium fractionation during vapor condensation is removed. Each seasonal cycle has the annual mean removed. The grid cells shown here are located at (a) 37.142°N 123.75°W; (b) 40.952°N, 125.625°N; (c) 44.761°N, 125.625°W; and (d) 48.571°N 125.625°W. Each time series has the annual mean removed.

[25] The TAGLAT simulation is designed to quantify how the source of vapor affects the simulated seasonal δ18Op cycle (process 2). Recall both observed and modeled δ18Op drop during winter months, so if seasonally varying winds transport tropical moisture with higher δ values toward the west coast in summer and lower δ values from middle latitudes in winter [Field et al., 2010b], it could explain the seasonal δ18Opcycle. However, the TAGLAT simulation reveals an opposite source-related influence on the average simulated seasonalδ18Op cycle. The fraction of precipitation with tropical tags has a maximum along the west coast during winter, rather than summer months (Figure 8a). Conversely, the fraction of precipitation advected from the Gulf of Alaska to the western U.S. coast reaches a maximum in summer (Figure 8b) and decreases in winter. Both of these variations are inconsistent with the seasonal source effect described above. Additional tagging simulations, with slightly modified tagging region boundaries, confirm these results are robust (not shown). This seasonal shift in moisture advection is related to the clockwise flow around and position of the North Pacific high (descending branch of the Hadley Cell), as the Hadley Cell migrates south during the Northern Hemisphere winter.

Figure 8.

Long-term mean (1953–2010) results from tagging simulations, showing mean seasonal variations of fraction of precipitation with (a) tropical, (b) middle latitude, (c) lower level, and (d) upper level tags. Results are from grid cells that run north-south along the western U.S./Canadian coast; the same grid cells inFigure 3.

[26] Simulated seasonal variations in the oxygen isotopic composition of column-integrated vapor in the troposphere, or precipitable water (δ18OPW), are consistent with the variations in tagged precipitation fraction (and tagged precipitable water fraction) from the TAGLAT simulations (compare Figure 3b with Figures 8a and 8b). For grid cells along the west coast of the U.S., IsoGSM simulates a maximum in δ18OPW in the winter/early spring (Figure 3b), which is when tropical (middle latitude) tagged precipitation fraction is highest (lowest) along the coast. Similarly, the minimum in δ18OPWthat occurs during summer coincides with the minimum (maximum) in tropical (middle latitude) tagged precipitation fraction. Satellite data have shown clear north-south gradients in vaporδ values over the North Pacific [Worden et al., 2007; Brown et al., 2008; Risi et al., 2012], and the model results presented here underscore the large influence the source region has on the isotopic composition of vapor advected to the west coast. This relationship is important for interpretations of climate proxies that are partially dependent on the isotopic composition of vapor (e.g., tree cellulose δ18O values [Berkelhammer and Stott, 2008, 2009]).

[27] This seasonal source effect, however, does not translate over to the simulated isotopic composition of precipitation. The reason why the source effect is not apparent in the simulated seasonal δ18Op cycle stems from the much greater isotopic effect that condensation height has on the isotopic composition of precipitation (process 3). Previous studies [Ehhalt, 1974; Ehhalt et al., 2005; Sayres et al., 2010; Risi et al., 2012] have shown the existence of vertical gradients in vapor δ values (as in Figure 6), where vapor is more depleted higher in the troposphere. Results from the TAGLEV simulation reveal that along the west coast (with the exception of southern California), the fraction of precipitation derived from the upper troposphere peaks during the winter months and is the lowest during summer months (Figure 8d). On the other hand, the fraction of precipitation with the lower troposphere tag is lowest during the winter, and peaks during the summer months (Figure 8c). The variations shown in Figures 8c and 8d are consistent with observed and modeled vertical gradients in δ18Ov and a seasonally varying “condensation height effect.” These results suggest that wintertime storms are tapping into vapor that is depleted in 18O from high in the atmosphere, resulting in a simulated drop in δ18Op during winter months.

[28] In southern California (red curve in Figures 3 and 8), IsoGSM simulates the lowest δ18Op during July and September, which is also observed in some stations in southern California (e.g., Santa Maria). These seasonally low values in simulated δ18Op coincide with months when a larger fraction of precipitation carries the upper level tags. Indeed, summer precipitation in southern California, though not as frequent, is largely a result of local convective plumes that result from intensified surface heating and vertical instability. These convective plumes are not as frequent in regions further to the north, where low level marine layer precipitation dominates summer months. Nonetheless, the low δ18Op during July and September coincide with months where the fraction of precipitation with upper (lower) level tags is high (low).

5.2. First Order Budget Calculations

[29] In order to assess whether or not the vertical isotopic gradient and temporal changes in tagged fraction are large enough to explain the seasonality of δ18Op within IsoGSM, an isotopic budget is calculated. The isotopic composition of precipitation is approximated by:

display math

where p1 and p2 are precipitation totals from tags 1 and 2 (corresponding to two different locations), δv1 and δv2 are the isotopic compositions of vapor within the first and second tagged regions, respectively, and ε is an oxygen isotopic equilibrium fractionation factor (assumed in this calculations to be a constant 10 ‰). Equation (1) can be simplified by defining the fractions: f1 = p1/(p1 + p2) and f2 = p2/(p1 + p2), yielding:

display math

The intent of this calculation is to isolate the contribution of condensation height on δ18Op. As such, the isotopic composition of vapor is assumed to be constant in time; thus removing the influence of horizontal moisture advection on δ18Ov and subsequently δ18Op (which will be addressed in another set of calculations). However, it is likely that δv1 and δv2 have seasonal variations associated with moisture transport [Berkelhammer et al., 2012].

[30] Values of δv1 and δv2are calculated using tag-weighted averages ofδ18Ov from the CTRL simulation, and f1 and f2 are calculated by dividing tagged precipitation by the total of tagged precipitation (making the two values sum to one) from the TAGLEV simulation. At a grid cell located near Santa Cruz, California (orange curve in Figures 3 and 8), the values of δv1 and δv2 are −16 ‰ and −31 ‰, respectively, while July values of f1 and f2 are 0.75 and 0.25, respectively. These values result in an estimated isotopic composition of −9.75 ‰ in July when δ18O values are highest. In January, when isotopic values are lowest, f1 and f2 change to 0.33 and 0.67, respectively, yielding an isotopic estimate of δp = −16.1 ‰. Thus, equation (2) estimates a seasonal variation at 37.1°N of −6.3 ‰, based on variations in condensation height alone with no seasonal change to the isotopic composition of vapor. The overestimation relative to observed and simulated amplitudes is due to factors that are not represented in equation (2), such as seasonal variations in δ18Ov. Additional calculations were carried out for grid cells located near Glendale, Oregon and Sooke Lake, British Columbia (green and blue curves in Figures 3 and 8), which resulted in an estimated seasonal change of −4.7 ‰ and −3.6 ‰, respectively. These additional calculations show that the large influence of seasonal condensation height variations on simulated precipitation δ values is not unique to California, but the influence (though slightly reduced) extends northward up the coastline.

[31] Equation (2) can also be applied to the TAGLAT simulation to understand the influence of variations in moisture source on the simulated seasonal δ18Op variations. For this case, δv1 and δv2are non-local and estimated by the annual average ofδ18OPW within the two tagging regions (Figure 5a), which are −17 ‰ and −23 ‰, respectively. Using the same Santa Cruz location, the values of f1 and f2 (calculated from the TAGLAT simulation) for July are 0.27 and 0.73, respectively, which results in an estimate of −11.4 ‰. The same respective values of f1 and f2 for January are 0.59 and 0.41. These January values yield a first order estimate of −9.5 ‰ and a seasonal isotopic increase of 1.9 ‰. Thus, the role of seasonal variations in tropical versus middle latitude moisture advection on the simulated seasonal cycle acts to increase δp from summer to winter (via changes to local δv). The change in moisture source can be seen in the seasonal cycle of the oxygen isotopic composition of precipitable water (δ18OPW) along the western U.S. coast, shown in Figure 3b. These results reveal that horizontal moisture advection slightly counteracts the role of condensation height on the simulated seasonal δ18Opvariations. However, the influence of condensation height outweighs that of moisture advection, and the result is a seasonal summer-to-winter decrease inδ18Op of 2 to 4 ‰. These model results imply that the increase in upper level condensation/precipitation (where vapor is most depleted in 18O) is the main cause of the simulated seasonal drop in δ18Op during winter months along the western U.S. coast.

5.3. Vertical Winds, Diverging Flow, and the Polar Jet

[32] Increased condensation and rainout of upper/middle tropospheric vapor that is isotopically more depleted in 18O relative to near surface vapor, is found here to be the primary influence on the simulated seasonal δ18Op variations. However, it is not immediately obvious what would cause the seasonal variations in condensation height, which is the intent of this section. These findings are consistent with Lin et al. [2009], who found from satellite data that off the California coast cloud top and cloud base heights are lower during summer months than winter months. Similarly, Kubar et al. [2012]using MODIS-aqua data and ECMWF-interm reanalysis data found that the fraction of low-level clouds decreases from summer to winter months along the west coast of the U.S.

[33] Figure 9a shows the mean simulated seasonal cycle of the 500 mb vertical pressure velocity (ω500) weighted by daily precipitation totals (negative values indicating upward motion). Indeed, values are the most negative during the winter months for all locations, except the southern California grid cell, an indication that air is still rising and condensing above the 500 mb level. These vertical motions are primarily a result of the horizontal winds, which are tightly constrained by the NCEP/NCAR Reanalysis wind fields via the spectral nudging technique. For central and northern California, Oregon, and southern Washington, summer ω500 are positive, an indication that air is moving downward and likely not condensing above 500 mb. Indeed, the seasonal profiles of simulated vertical motion (Figure 10a shows the amount weighted profile for two locations) indicate that during winter months most of the tropospheric column is rising during rain events. Vertical motion for summer rain events only occurs in the lower troposphere (Figure 10a). However, this is not the case for the southern California grid cell (red curve in Figure 9a), where modeled ω500 is on average negative during July and September rain events.

Figure 9.

Long-term mean (1953–2010) seasonal cycles of (a) 500 mb vertical pressure wind and (b) 200 mb divergence. Each monthly mean is weighted by daily precipitation totals. Results are from grid-cells that run north-south along the western U.S./Canadian coast.

Figure 10.

(a)Long-term mean (1953–2010) precipitation-weighted vertical profiles of vertical pressure velocity for two locations during January (blue) and July (red) with units of Pa s−1. (b) The same plot for horizontal divergence with units of s−1. Solid curves represent the west coast at the 37.142°N, latitude and dashed curve show the 40.952°N latitude.

[34] Figure 9b shows seasonal cycles of 200 mb divergence, weighted by daily precipitation amount in IsoGSM (as were the curves in Figures 9a and 10a). These curves show a sharp increase in upper level divergence above the western U.S. coast during early winter. Not surprisingly, 1000 mb divergence is negative (i.e., air is converging) and the lowest during the winter months (Figure 10b). Maintaining hydrostatic balance, the wintertime vertical motion (shown in Figures 9a and 10a) is a response to divergence aloft and convergence near the surface. This is not the case for summer months, when the vertical divergence profile is almost reversed, indicating that diverging flow is suppressing summertime rising motion. The slight rising motion that occurs in the lowest levels is likely a result of enhanced summer heating and vertical instability. The seasonal variations in divergence is in part associated with the wintertime intensification of the polar jet stream [Reiter, 1963], which can be seen in the seasonally varying 200 mb wind speeds (precipitation weighed), as the velocities increase from summer to winter (Figure 11). Wintertime upper level divergence is also a result of seasonal changes in the wind curvature. Figure 11 shows 200 mb wind speeds and geopotential height contours weighted by precipitation for two locations along the western U.S. coast for July and January. In July, the distance between geopotential height contours decreases moving eastward toward the coast, indicating convergence. In January, the flow is more zonal, and the geopotential contours slightly diverge from one another toward the coast. These model results reveal a seasonal link between the isotopic composition of precipitation and the strength and curvature of the polar jet stream via upper tropospheric divergence, vertical vapor transport, and condensation height.

Figure 11.

Spatial pattern of the long-term mean (1953–2010) 200 mb wind speeds (filled contours) and geopotential heights. Each field is weighted by daily precipitation totals from one western U.S. location, indicated by the red asterisk: (a and c) central California and (b and d) southern Oregon. Figures 11a and 11b are for July, and Figures 11c and 11d are for January. Wind speed contours are in unit of 2 m s−1, and height contours are in units of 0.1 km.

6. Summary and Discussion

[35] The long-term mean seasonal cycles of precipitationδ18O values from 13 stations along the western U.S. coast share three common features: (1) highest δ18Op in summer and lower values during winter [Ersek et al., 2010]; (2) reduced seasonal amplitudes at the northern stations; and (3) a shifted maximum later in the season at the northern stations. All three of these observed features are reproduced in an unperturbed IsoGSM simulation. The model is also able to accurately capture many of the sub-seasonal variations in the observations, though this was not the case for all stations. Sensitivity experiments indicate equilibrium oxygen isotopic fractionation during condensation (or Rayleigh distillation) gives rise to simulated seasonal variations inδ18Op along the western U.S. coast. When the fractionation factor (αeq-con) is set to 1.0 (which also removes the temperature dependence), the simulated seasonal isotope variations almost completely vanish. The sensitivity experiments also indicate no influence of the temperature-dependence ofαeq-con or αeq-ev, which contrasts with the assertions made by V10a.

[36] Further simulations revealed that approximately 60% of the modeled winter precipitation condenses in what is defined as the upper/middle troposphere (Figures 8d), where vapor is depleted in 18O relative to lower elevations (Figure 7). In contrast, in the summer only about 25% of the modeled precipitation is derived from the upper/middle troposphere and about 75% condenses in the lower troposphere (except for parts of southern California), where vapor is less depleted in 18O. Simple budget calculations confirm that vertical isotopic gradients of vapor and seasonal variations in the condensation height have the largest influence on the simulated seasonal δ18Op cycle in the western U.S., which is the main finding of this study. This result is similar to the findings of Scholl et al. [2009], who found a correlation between cloud heights and δ18Op seasonality in Puerto Rico. Furthermore, Coplen et al. [2008]found (on sub-hourly timescales) along the California coast that periods of lowδ18Op values coincide with periods when vapor was condensing above bright band altitudes where seeder precipitation typically forms.

[37] The methods used in this study to understand the regional isotopic variability differ from other studies that also used atmospheric GCMs to better understand variations in precipitation δ values, such as Pausata et al. [2011] and Lewis et al. [2010]who looked at the isotopic influences of moisture-source variations and regional rainout.Figure 12 shows a schematic flowchart of the methodology used here and the general results of the modeling experiments. Though this particular study focused on the western U.S., the same modeling methodology could be applied to other regions to fingerprint the primary influences of variability in δ18Op.

Figure 12.

Schematic diagram depicting the modeling methodology used to find the precise cause of isotope seasonality in the western U.S. The figure also shows the general modeling results.

[38] Assuming the mechanism that gives rise to the seasonal δ18Opcycle within IsoGSM is true in the real world, the findings here have two important implications. First, seasonal variation of the isotopic composition of precipitation should be interpreted as changes in condensation height in the western U.S. This refers to both the interpretation of direct measurements of precipitation samples and from precipitation derived climate proxies with sub-annual resolution. This applies only to regions close to the western U.S. coast, and future studies should investigate if the same mechanisms cause the variations observed in the interior of the U.S. (where the seasonality is the greatest [Vachon et al., 2007; Bowen, 2008; Feng et al., 2009]) and along the eastern U.S. coast.

[39] Second, the large influence of condensation height on δ18Op seasonality creates an extra layer of complexity when interpreting interannual climate proxy records based on isotopes in precipitation, whether it be from speleothems [Oster et al., 2009], tree cellulose [Berkelhammer and Stott, 2008, 2009], or leaf wax n-alkanes [Feakins and Sessions, 2010; Romero and Feakins, 2011]. It is probably incorrect to assume that the seasonality of condensation height is static, and if the seasonal statistics vary from year to year, it will likely cause interannual variations in the isotopic composition of precipitation. Further work should focus on what controls the year-to-year changes in the isotopic composition of precipitation along the western U.S. coast, and whether condensation height is affecting the interannual and interdecadalδ18Op variations. If the same mechanism (condensation height) is affecting the interannual δ18Op variations, then the proxy record should be interpreted as such.

[40] On the other hand, if the influence of condensation height on interannual isotopic variations is small (or cancel out on decadal timescales), it is likely that the quantity δ18Op contain information about moisture advection (i.e., a source effect). If this were the case, the quantity δ18Op could prove to be a valuable tool in coming decades. For instance, the global climate model simulations conducted as part of the Intergovernmental Panel on Climate Change Fourth Assessment Report project a poleward shift in middle latitude storm tracks in response to rising tropospheric temperatures in the middle to high latitudes [Yin, 2005; Salathé, 2006]. These studies reveal that much of the western U.S. could be at risk because water resources in this region are already stressed due to an inadequate supply and growing demand. If interannual and interdecadal precipitation δ values do indeed trace the advected moisture source and if shifts were to take place in northern middle latitude storm tracks, it might be detectable through isotope measurements. Thus, understanding the causes of the isotopic interannual variations along the western U.S. coast is of clear importance.


[41] We acknowledge with gratitude Christopher Lehmann and the National Atmospheric Deposition Program (NADP) for providing precipitation samples. We recognize Miguel Rincon and Jack Zang for their help with the isotope analysis of NADP samples. We also thank Tyler Coplen and two anonymous reviewers for their helpful comments and suggestions that improved the quality of this manuscript. Funding for this work was provided by the NOAA/CPO Climate Change & Detection Program: Paleoclimate Studies (grant NA10OAR4310129). This work was also supported by a grant from the National Science Foundation (award AGS-1049238).